An Improved Ghost-YOLOv5 Infrared Target Detection Algorithm Based on Feature Distillation

NAVIGATE

Table of Contents

STATISTICS

Viewed3260

Downloads1142

An Improved Ghost-YOLOv5 Infrared Target Detection Algorithm Based on Feature Distillation

[HTML] PDF下载 (1142)

[1]LI Beiming,JIN Ronglu,XU Zhaofei,et al.An Improved Ghost-YOLOv5 Infrared Target Detection Algorithm Based on Feature Distillation[J].Journal of Zhengzhou University (Engineering Science),2022,43(01):20-26.[doi:10.13705/j.issn.1671-6833.2022.01.013]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43卷 Number of periods: 2022 01 Page number: 20-26 Column: Public date: 2022-01-09

Title:: An Improved Ghost-YOLOv5 Infrared Target Detection Algorithm Based on Feature Distillation

Author(s):: LI Beiming¹; JIN Ronglu¹; 2; XU Zhaofei²; LIU Qing²; WANG Shuigen²; 1.College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;
2.Yantai IRay Technology Co., Ltd., Yantai 264000, China

Keywords:: infrared target detection; data enhancement; model pruning; feature distillation; Hisi plat-form

CLC:: TP391.9

DOI:: 10.13705/j.issn.1671-6833.2022.01.013

Abstract:: Infrared target detection algorithms suffered from problems such as poor adaptability and high computational complexity. An improved Ghost-YOLOv5 infrared target detection algorithm was proposed based on feature distillation to solve the above problems. Firstly, GhostNet block was used for backbone pruning. Se-condly, two effective data enhancement methods including Mosaic and Copy-paste were used, together with feature distillation to improve the accuracy in object detection. Furthermore, an infrared image dataset that contained a variety of scenarios with pedestrians, motor vehicles, and non motorized targets was constructed. The test experimental results on the above dataset showed that the model parameters obtained by the algorithm proposed in this paper using GhostNet module were only 1.9M, and the accuracy of the small model on the infrared dataset were improved by 6.6% through feature distillation and data enhancement. And the overall mAP value reached 90.1%. The detection speed of the model could reach 25 frames per second and the average detection accuracy could reach 90.2% when measured empirically on Hisi, all achieving higher detection accuracy compared to a variety of common models portable to this platform.

References:: ［1］张敏，韩芳，康键，等．红外热成像技术在民用领域的应用［J］．红外，2019，40( 6) : 37－45．
［2］陈华，周晓巍．红外热成像技术在通信电源电路板故障检测中的应用［J］．通信电源技术，2021，38 ( 1) : 114－116．

［3］王洪琳，郑睿．激光雷达在轨道列车自动驾驶系统中的应用研究［J］．江苏科技信息，2021，38 ( 31) : 42－44，55．

［4］蔡军，黄袁园，李鹏泽，等．基于视觉对比度机制的红外弱小目标检测算法［J］．系统工程与电子技术， 2019，41( 11) : 2416－2423．

［5］ KIM S，YANG Y，LEE J，et al． Small target detection utilizing robust methods of the human visual system for IＲST［J］．Journal of infrared，millimeter and terahertz waves，2009，30( 9) : 994－1011．

［6］ SHAO X P，FAN H，LU G X，et al． An improved infrared dim and small target detection algorithm based on the contrast mechanism of human visual system［J］． Infrared physics ＆ technology，2012，55( 5) : 403－408．

［7］ CHEN C L P，LI H，WEI Y T，et al．A local contrast method for small infrared target detection［J］． IEEE transactions on geoscience and remote sensing，2014， 52( 1) : 574－581．

［8］张震，李浩方，李孟洲，等．改进 YOLOv3 算法与人体信息数据融合的视频监控检测方法［J］．郑州大学学报( 工学版) ，2021，42( 1) : 28－34．

［9］崔娟．红外偏振成像技术在船舶目标探测中的应用［J］．舰船科学技术，2020，42( 18) : 82－84．

［10］ DU L，GAO C Q，FENG Q，et al．Small UAV detection in videos from a single moving camera［C］/ /Communications in Computer and Information Science． Cham: Springer，2017: 187－197．

［11］ CHEN P H，LIN C J，SCHÖLKOPF B． A tutorial on ν-support vector machines ［J］． Applied stochastic models in business and industry，2005，21 ( 2) : 111 －136．

［12］汪慎文，张佳星，褚晓凯，等．两阶段搜索的多模态多目标差分进化算法［J］．郑州大学学报( 工学版) ， 2021，42( 1) : 9－14，110．

［13］周贵华，许丽娟，周伟昌．基于深度学习的红外序列图像小目标检测方法研究［J］．激光杂志，2020，41 ( 12) : 61－64．

［14］ HE K，ZHANG X，ＲEN S，et al． Spatial pyramid pooling in deep convolutional networks for visual recognition［J］． IEEE transactions on pattern analysis and machine intelligence，2015，37( 9) : 1904－1916．

［15］ LIN T Y，DOLLAＲ P，GIＲSHICK Ｒ，et al．Feature pyramid networks for object detection［C］/ /2017 IEEE Conference on Computer Vision and Pattern Ｒecognition ( CVPＲ) ．Piscataway: IEEE，2017: 2117－2125．

［16］ LIU S，QI L，QIN H F，et al．Path aggregation network for instance segmentation［C］/ /2018 IEEE/CVF Conference on Computer Vision and Pattern Ｒecognition． Piscataway: IEEE，2018: 8759－8768．

［17］陈寂驰，魏国华，郭聪隆，等．一种基于红外图像序列的深度学习三维重建仿真方法初探［J］．空天防御，2020，3( 4) : 21－29．

［18］ HAN K，WANG Y H，TIAN Q，et al． GhostNet: more features from cheap operations［C］/ /2020 IEEE/CVF Conference on Computer Vision and Pattern Ｒecognition ( CVPＲ) ． Piscataway: IEEE，2020: 1580 －1589．

［19］ YIM J，JOO D，BAE J，et al．A gift from knowledge distillation: fast optimization，network minimization and transfer learning［C］/ /2017 IEEE Conference on Computer Vision and Pattern Ｒecognition ( CVPＲ) ． Piscataway: IEEE，2017: 4133－4141．

［20］ WANG C Y，BOCHKOVSKIY A，LIAO H Y M．ScaledYOLOv4: scaling cross stage partial network［EB/OL］． ( 2020－11－ 16) ［2021 － 05 － 06］． https: / /arxiv． org / abs/2011. 08036．

［21］ＲUDEＲ S．An overview of gradient descent optimization algorithms［EB/OL］． ( 2016－09－15) ［2021－05－06］． https: / /arxiv．org /abs/1609. 04747．

Similar References:

Memo

Last Update: 2022-01-09